Multi-surface optical 3D microscope

ABSTRACT

A method of detecting multi-surfaces of an object includes providing an imaging system capable of detecting surfaces of the object. After system parameters are set up, two-dimensional images of the object at multiple Z steps can be acquired. Each surface of the object can then be extracted using two steps. In a first step, the surface can be constructed based on a confidence threshold. In a second step, the surface can be enhanced using an interpolation filter.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application61/378,794, entitled “MULTI-SURFACE OPTICAL 3D MICROSCOPE”, filed onAug. 31, 2010, and incorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention is related to optical three dimensional (3D) measurementtechniques, and more particularly to methods of generating anddisplaying multiple-surface 3D images.

2. Description of the Related Art

The ability to measure a 3D object accurately and to render a 3D imageof it on a two-dimensional (2D) display is very important to a varietyof academic and industrial applications. For example, a bio-chip usedfor DNA sequencing may contain thousands of minute wells partiallyfilled with reagent and sealed with a thin layer of plastic cover.Because reagent is expensive, it is important to measure the fill levelinside a well so that reagent waste can be eliminated. The challenge inthis case is to accurately profile the many surfaces involved, namelythe surface of plastic cover, the top surface of the well, the fluidsurface of the reagent, and the bottom surface of the well, and todisplay the 3D structure and measurement results in a way that is easyto understand.

Over the years, several types of optical based non-destructivemeasurement systems have been developed to address the aforementionedapplications. These systems are typically based on techniques such asconfocal microscopy and structured light sources (SLS).

For example, U.S. Pat. No. 4,198,571 issued to Sheppard in 1980discloses the basic technique of confocal microscopy. U.S. Pat. No.5,022,743 (Kino) discloses an improved confocal system using a Nipkowdisk. U.S. Pat. No. 5,065,008 (Hakamata) describes a confocal systembased on laser scanning. U.S. Pat. No. 6,838,650 (Toh) describes animproved high speed and high resolution confocal system for threedimensional measurement. U.S. Pat. No. 7,372,985 (So) discloses aconfocal based system and method for volumetric 3D displaying ofbiological tissue.

Systems based on structured light sources (SLS) offer similar capabilityto that provided by confocal microscopy. For example, U.S. Pat. No.7,729,049 (Xu) describes a 3D microscope using one of various SLStechniques.

Both confocal microscopy and SLS 3D measurement systems generate a 3Dimage by capturing multiple 2D images at a set of Z steps within a Zscan range. In the case of a confocal system, an algorithm based onmaximum image intensity is used to determine a surface. In an SLSsystem, such as the one disclosed in U.S. Pat. No. 7,729,049 (Xu),maximum image contrast is used instead. Because both confocal and SLSsystems can image through optically translucent materials, any interiorsurfaces inside a transparent object can, in principle, be measured. Forexample, U.S. Pat. No. 7,323,705 (Haga) discloses a method and apparatusto measure liquid volume of small bio-cells by measuring the top surfaceof the liquid and the bottom of the well. However, Haga only measures anaverage value for each of the surfaces, and not the 3D profile of thesurfaces. U.S. Pat. No. 7,227,630 (Zavislan) discloses a confocal systemthat can produce vertical sections of a sample by displaying the variousinternal parts using image intensity values, but does not create animage in the form of extracted surfaces.

In practice, profiling an interior surface of a transparent object isnot trivial. For example, it is difficult for an optical 3D measuringsystem to find the boundary surface between two liquids with similaroptical properties. Furthermore, a transparent object could addaberration to the system optics to produce undesirable artifacts.Because of these difficulties, internal surfaces extracted by aconventional 3D measuring system often contain false surfaces anddemonstrate various degrees of image distortion. Without effective meansfor separating between false and valid surfaces, a conventional 3Dsystem will not be able to present the true internal structure of anobject.

U.S. Pat. No. 7,372,985 (So) discloses a system combining confocaloptics with direct volumetric rendering for imaging tissue samples. Thevolumetric data generated by this system is a collection of pixel valuesat a regular XYZ grid obtained by stacking a set of sequentiallycaptured 2D images. In the direct volumetric rendering scheme, stacked2D image pixel values are directly mapped into 3D. Various segmentationmethods have also been suggested to enhance the volume image byseparating valid image from noise.

Instead of direct volumetric rendering, U.S. Pat. No. 6,556,199 (Fang)discloses a method and apparatus to convert intensity volume data setinto a voxel-based volume representation. A voxel-based (i.e. avolumetric pixel-based) volumetric display of 3D images has beenavailable as a computer infrastructure is well known. For example, U.S.Pat. No. 6,940,507 (Repin) discloses such a volume rendering processwith fast rendering time and improved visual image quality. Compared todirect volumetric rendering, voxel-based volumetric rendering offerflexibility in displaying the volume data. For example, it can extractinternal surfaces of an object as well as display 2D pixel values in 3D.While a voxel based 3D measurement system may be ideal for viewingbiological specimens with many irregular internal parts, it is notoptimized for industrial parts that have well-defined internalstructures.

For an industrial part, such as a micro-fluidic circuit or a bio-chip,it is highly desirable to view all of its surfaces in one 3D image andto profile these surfaces in precision. The 3D systems that offer directvolumetric rendering do not generate surfaces, so they are of limitedutility. Systems that use voxel-based volumetric rendering are notoptimized for industrial parts due to their limited surface extractionprecision, slow speed of rendering, and lack of interactive surfaceselection.

Therefore, a need arises for a technique to generate accurate 3D imageof an object, to render the object in multi-surface 3D view, and tomeasure various parameters of these surfaces.

SUMMARY OF THE INVENTION

A method of detecting multi-surfaces of an object is described. In thismethod, an imaging system capable of detecting surfaces of the object isprovided. After system parameters are set up, two-dimensional images atmultiple Z steps can be acquired. Using two steps, each surface of theobject can then be extracted. In a first step, the surface can beconstructed based on a confidence threshold. In a second step, thesurface can be enhanced using an interpolation filter.

The system parameters can include at least a number of surfaces, a Zscan step size, a confidence threshold, a start Z scan position, and a Zrange of each surface. The imaging system can be a structured lightsource based imaging system or a confocal imaging system. The confidencethreshold and the interpolation filter can be based on one of imagecontrast values and image intensity values. The interpolation filter canbe determined by a filter length as well as an image contrast percentilethreshold or an image intensity percentile threshold.

An enhanced surface can include first data points below the confidencethreshold and second data points above the confidence threshold.Notably, the second data points generally neighbor the first datapoints. Therefore, in one embodiment, the first data points can beassigned new contrast and Z profile values, which can be derived from acontrast-weighted average of the second data points. In anotherembodiment, the first data points can be assigned new contrast and Zprofile values, which can be derived from an intensity-weighted averageof the second data points.

A method of displaying multi-surfaces of an object is also described. Inthis method, the opacity of a particular surface of the object can becontrolled. The controlling can be determined by an opacity parameterselected by an operator using an interactive software slider bar.Additionally, real surface data can be separated from noise data duringthe rendering of the particular surface. The separating can bedetermined by a filter selected by an operator using another interactivesoftware slider bar. The filter can be based on image contrast orintensity.

A method of measuring multi-surfaces of an object is also described. Inthis method, a plurality of cross-sectional profiles of surfaces of theobject can be selected for simultaneous display using a single x-y plot.In one embodiment, selecting can be done using an interactive menuwithin a graphic user interface. At least one of the cross-sectionalprofiles can be corrected. The correcting can include converting anapparent thickness of a layer associated with a cross-sectional profileinto an actual thickness. For example, the actual thickness of a layercan be computed by multiplying the apparent thickness of the layer byits index of refraction (when known).

Parameters of the object can be computed in this method. The parameterscan include at least two of surface thickness, curvature, roughness, andwaviness. In one embodiment, the parameters can include an index ofrefraction.

The plurality of cross-sectional profiles as well as results of thecorrecting and the calculating can be displayed in this method. Thedisplaying can include providing at least one cursor for the x-y plot.Each cursor can determine the values of a table showing the results foreach layer of the object having an associated profile

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A illustrates an exemplary imaging system including a 3-D opticalmicroscope.

FIG. 1B illustrates an exemplary technique for creating a multi-surface3D image.

FIG. 2 illustrates an exemplary technique for multiple surfaceextraction.

FIG. 3 illustrates an exemplary interpolation technique usable in aninterpolation filter.

FIG. 4 illustrates an exemplary interactive user interface to controlthe opacity and filter.

FIG. 5 illustrates an exemplary screen shot of a rendering of amulti-surface 3D object.

FIG. 6 illustrates a screen shot of exemplary, displayed profilesassociated with multiple object layers. The profiles are simultaneouslydisplayed on a single x-y plot, which includes interactive cursors fordetermining the display of various layer results.

DETAILED DESCRIPTION OF THE FIGURES

State of the art 3D imaging and measurement techniques are set forth incommonly assigned U.S. Pat. No. 7,729,049 and U.S. Pat. No. 7,944,609,and co-pending U.S. Published Applications 2010/0135573, and2008/0291533, the contents of which are incorporated herein byreference.

FIG. 1A illustrates an exemplary imaging system 100 including a 3-Doptical microscope having a pattern generator 101. Knob 103 can be usedfor focusing adjustment, which can be adjusted by electrical motor,piezoelectric actuator, or other means. A camera 102 can be used forimage acquisition. A processor 104 can be used to control focusingadjustment knob 103, camera 102, and pattern generator 101. Processor104 can also analyze data and create a 3-D image of the sample. In oneembodiment, processor 104 can include a personal computer. Note thatimaging system 100 and other structured light source imaging systemsthat can be used to create a multi-surface 3D image are described indetail in U.S. Pat. No. 7,729,049 (Xu).

FIG. 1B illustrates an exemplary technique 110 for creating amulti-surface 3D image. In technique 110, step 111 can get the scanparameters needed for the acquisition of a multi-surface 3D image. Theseparameters can be entered into the imaging system, either throughloading of a recipe or by user input. Exemplary parameters can includethe number of surfaces, the Z step size, the total Z steps, the startand stop Z scan positions of each surface, and the filter threshold foreach surface. Step 112 can move the sample (or, alternatively, theobjective) to a start Z scan position, i.e. the Z step set as 0.

Step 113 can use the imaging system to capture two 2D images: one 2Dimage with the pattern generated by the structured light source, andanother 2D image without the pattern while still illuminated. At thispoint, the imaging system can calculate the image contrast from thepatterned image. Both images, the calculated image contrast, and thestep number can be stored in memory. Step 114 can move the sample (orthe objective) one Z step toward the stop Z scan position. Step 115 canincrease the Z step count by 1. Step 116 can determine whether the stopZ scan position is reached. If not, then technique 110 can return tostep 113 and perform the corresponding actions at the current Z step.When the stop Z scan position is reached, then step 117 can extractmultiple surfaces from the stored images.

In a second embodiment, which uses a confocal imaging system for dataacquisition, step 113 can be replaced by step 113A. In this case, theimage intensity (not the image contrast) can be calculated and only oneimage, i.e. the image without the pattern, is saved (not both images).

FIG. 2 illustrates an exemplary technique for step 117, i.e. multiplesurface extraction. Step 201 retrieves the images, contrasts, andsurface parameters. Exemplary surface parameters include the number ofsurfaces, the start and stop positions of each surface, and the filterthresholds for all surfaces. Step 202 sets the surface count to 0. Then,step 203 uses a maximum contrast technique to extract one surface. Anexemplary maximum contrast technique is disclosed in U.S. Pat. No.7,729,049 (Xu). In one embodiment, instead of searching through theentire Z scan range, just the start and stop positions associated withthe surface can be used. Note that for a confocal system, step 203A canuse a maximum intensity technique instead of the maximum contrasttechnique. Step 203 can filter the extracted surface using aninterpolation filter with a threshold for the surface being extracted(described in further detail below). Step 205 can increment the surfacecount. Step 206 can determine whether the surface count is equal to thenumber of surfaces specified in step 201. If not, then the technique canreturn to step 203. If so, then all specified surfaces have beenextracted (end extraction).

In the maximum contrast technique, a clearly visible surface point isone that is associated with a surface data point with consistent Zprofile value and high contrast. For the part of the surface that is notclearly visible, the maximum contrast technique produces a noisy Zprofile with low contrast surface data points. Because the contrastvalue of a surface data point relates to the visibility of the surface,it is a measure of confidence for a visible surface. Note that whenusing the maximum intensity technique, the intensity would be used asthe measure of confidence.

In some samples, only part of a valid surface is clearly visible to thesystem. As a result, the extracted surface will have consistent Z valueswith high confidence data points where the surface is clearly visible,and noisy Z values with low confidence data points where the surface isnot. Notably, the noisy data points are not a good representation of thephysical surface. The above-described interpolation filter (step 204)can advantageously fill-in the noisy area to produce a surface that is abetter approximation to the physical surface.

Because a physical surface typically changes shape smoothly, the Z valueof one data point on a surface gives a good indication of the Z positionof its neighbor. As a result, a high confidence surface data point canbe used to modify its neighboring low confidence data points to helpfill-in, or smooth-out a noisy surface area. Therefore, one step inimproving the extracted surface is to identify the high confidentsurface data points.

Depending on the expected size of the valid surface area as a percentageof the total image area, a predefined layer specific threshold ofcontrast (or intensity) percentile is used to select the high confidencedata points from the extracted data. For example, if the valid surfaceoccupies only 5 percent of the total image area, then data points with a95 percentile confidence would most likely be within the valid surfacearea. Setting a threshold of 95 percentile will likely eliminate mostdata points and select only those within the valid surface area. Ifthere are areas within the physical surface that are not clearlyvisible, then the data points from those area will have low confidence.However, they might be surrounded by high confidence data points. Theinterpolation filter can interpolate using such high confidence datapoints to generate an expected profile value for the low confidence(i.e. below threshold) data points. In doing so, each original noisy,low confidence area can be replaced with a more representative profilevalue. In general, this replacement results in the contrast (orintensity) values associated with the low confidence data points beingraised to that of their neighboring high confidence (or intensity) validsurface data points. As a result, the patchy and noisy extracted surfacecan be improved to accurately represent the real physical surface.

FIG. 3 illustrates an exemplary interpolation technique 300 usable inthe above-described interpolation filter. Inputs 301 for thisinterpolation filter can include the contrast percentile threshold andthe filter length. In general, technique 300 enhances the low confidencedata point by leveraging their neighboring high confidence data points.

Step 302 puts the contrast values of all data points into a 1D array andsorts the points in ascending order. Step 303 converts a percentile to acorresponding number of data points that need to be selected out of thetotal number of data points. Using that number of data points as anindex, the contrast threshold is then determined.

Note that other methods of converting percentile value to numeric valuecan also be used in other embodiments. For example, the histogram methodis another choice for the conversion. These methods are within the scopeof this invention.

After the contrast threshold is found, the low confidence data pointscan be separated from the high confidence data points. For each lowconfidence data point, both its contrast value and the Z profile valuecan be replaced by the contrast-weighted average of its neighboring highconfidence data points. In one embodiment, a neighborhood size of 9 canbe used, wherein the high contrast data points within a 9×9 blockcentering around the low confident data point can be used. In general, alarger block size requires more data processing and results in slowerthroughput, but provides better results for lower quality images. In oneembodiment of step 306, the contrast-weighted average Z profile value Z′can be defined as:

$Z^{\prime} = {{\frac{\sum\limits_{i,j}\left( {{Contrast}_{ij} \cdot Z_{ij}} \right)}{\sum\limits_{i,j}{Contrast}_{ij}}\text{:}\mspace{14mu}{Contrast}_{ij}} > {ContrastThreshold}}$where i,j are the indices of each pixel in the neighborhood centering atthe current data point. Note that the size of the neighborhood isspecified by the predefined filter length.

Step 304 sets the row counter (i=0) and column counter (j=0). Step 305determines whether the contrast value of the data point [i,j] is belowthat of the contrast threshold, which was chosen in step 303. If so,then step 306 can update that low confidence data point. During thisupdate, the Z profile value of data point [i,j] can be replaced with thecontrast-weighted average profile value using the above formula. Alsoduring the update, the contrast value of data point [i,j] can bereplaced with the contrast-weighted average. In one embodiment, the newcontrast value Contrast′ can be defined as:

${Contrast}^{\prime} = {{\frac{\sum\limits_{i,j}{Contrast}_{ij}}{\sum\limits_{i,j}1}\text{:}\mspace{14mu}{Contrast}_{ij}} > {ContrastThreshold}}$

Step 307 can determine whether all data points have been reviewed. Ifnot, then step 308 can increment the data point [i,j] in a raster scanmanner and return to step 305. Note that when step 305 determines thatthe contrast value of data point [i,j] is equal to or above the contrastthreshold, then technique 300 can proceed directly to step 307 (and skipstep 306). If all data points have been reviewed, as determined by step307, then technique 300 is done.

When a confocal system is used to implement technique 300, inputs 301Afor the low confidence data interpolation filter can be the intensitypercentile threshold and the filter length. For low confidence datapoints, both the Z profile value and the intensity value can be replaced(in the case of the intensity value, by the intensity-weighted averageof their neighboring high confidence data points) in step 306A.

Note that the resulting extracted multiple surfaces (step 107) can bedisplayed in various ways and using various hardware and software tools.In one embodiment, each surface can be rendered within the same 3D frameusing the computer industry standard OpenGL infrastructure. Otherinfrastructures such as Microsoft's Direct3D and other computer displaysoftware package can also be used.

Because each surface is extracted from the 2D image stack taken from asingle Z scan, its relative Z position reflects the sample's physicalstructure. In accordance with one exemplary display, from the bottomsurface and up, each of the multiple surfaces can be rendered with anopacity value. These opacity values are adjustable so that any surfacecan either be seen, highlighted, or hidden. Due to the fast renderingspeed of above-described multiple surface extraction, the opacity of therendered surfaces can be adjusted interactively.

Using a conventional method, an image of an extracted internal surfaceof an object usually covers the whole image area. Unfortunately, if theinternal surface does not physically cover the whole image area, thenthe extracted surface image may contain large amounts of false data madeof noise.

To solve this problem, a multi-surface 3D image display method canfilter out false data points. In the first embodiment described above,the surfaces are extracted using the maximum contrast method, andcontrast is used as a measure of confidence in determining a validsurface data point. In the second embodiment described above using aconfocal based system, the intensity is used as the measure ofconfidence. By using a contrast (or intensity) filter to determinewhether to render a surface data point, an analysis system can correctlydisplay a surface that only covers part of an image area. Because therendering speed of the multi-surface imaging system is in real time, theselective rendering can be done interactively, thereby allowing a userto interactively set a contrast (intensity) filter while watching thesurface being rendered.

FIG. 4 illustrates an exemplary interactive user interface 400 tocontrol the opacity and filter. In this example, the multi-surface 3Dimage has three surfaces. Therefore, three opacity and contrast (orintensity) filter slider bars can be provided, one pair for eachsurface. Each opacity slider bar can control the visibility of eachsurface while each contrast (or intensity) filter slider bar candetermine the rendering area on each extracted surface. It is understoodthat the number of opacity and contrast/intensity slider bars providedin interface 400 can be modified for any number of surfaces, i.e. withfewer surfaces and with more surfaces.

FIG. 5 illustrates an exemplary screen shot 510 of a rendering of amulti-surface 3D object. The object has a step structure 501 (e.g.copper) covered by a thin glass plate 502. Thus, there are threesurfaces involved, i.e. the top and bottom surfaces of the glass plate,and the surface of the copper step. Notice that dark coloredcontaminants on the top glass surface and copper surface are clearlyvisible. In one embodiment, screen shot 510 can also include interactiveuser interface 400 (FIG. 4) for user convenience.

Although rendering a multi-surface in 3D gives an overall view of asample's internal structure, a quantitative measurement of the structureis lacking. Note that in a conventional one-surface cross sectionalview, a single profile is displayed on a two dimensional chart where thex-axis shows the horizontal span and the y-axis shows the vertical span.In the multi-surface 3D image, this presents a challenge because forevery x-axis position there can be multiple values for the y-axis (eachvalue on the y-axis represents the location of a surface).

To solve this problem, the cross sectional profile for each of thesurfaces can be simultaneously displayed in a single x-y plot. Forexample, if 3 surfaces on the 3D image are provided, then 3 profilescorresponding to the 3 surfaces can be displayed. FIG. 6 illustrates anexemplary screen shot 600 of profiles 610, 611, and 612, wherein profile610 corresponds to a top surface, profile 611 corresponds to a middlesurface, and profile 612 corresponds to a bottom surface. Because eachsurface represents a distinct physical layer in the original object, thedistance between profiles 610 and 611 within the same cross-sectionaldisplay represents the apparent thickness of the top layer or theapparent distance between the top and middle surfaces as measured by the3D imaging system. Since most industrial samples are measured in ambientenvironment (i.e. in air), if the index of refraction n₁ of the toplayer is known, then the actual thickness of the top layer or thedistance between the top and middle surfaces can be computed as follow:Actual thickness of top layer=Apparent thickness of top layer×tan[arcsin(NA)]/tan [arcsin(NA/n ₁)where NA is the numerical aperture of the microscope objective lens usedin imaging.

Conversely, if the thickness of the top layer is known, then the indexof refraction of the top layer can be calculated from the measuredapparent thickness value. The thickness of other layers can be computedin a similar manner.

In addition to thickness measurement, other geometric parameters ofevery displayed surface can be measured as well. Exemplary parameterscan include surface curvature, roughness, waviness, etc.

Screen shot 600 can further include a table 614. Table 614 can includeone or more parameter results of each layer having a profile. Notably,these results can be modified based on the position of two pairs ofcursors: cursor pair 615 and cursor pair 616. Each pair of cursors (i.e.615 or 616) can be an interactive cursor position by a user. The cursorpair 615 generates the Cursor Left entries in table 614, whereas thecursor pair 616 generates the Cursor Right entries in table 614. Eachcursor pair can calculate/generate the average height of the profile aswell as the width/length in between the member cursors of the cursorpair (i.e. the left cursor of cursor pair 615 and the right cursor ofcursor pair 615.)

Note that a surface may not include the whole span of a cross section.In that case, a portion of each profile may be left blank because nocorresponding physical layer exists. This missing data presents an issuewhen the distance between two such profiles is measured. One solution isto disregard the missing data and only provide distance data atlocations where both profiles are available.

The embodiments described herein are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. As such, manymodifications and variations will be apparent. Accordingly, it isintended that the scope of the invention be defined by the followingClaims and their equivalents.

The invention claimed is:
 1. A method of detecting multiple surfaces ofan object, the method comprising: setting up system parameters of animaging system; acquiring two-dimensional images of each of the multiplesurfaces of the object at multiple Z steps as defined by the systemparameters; and extracting each of the multiple surfaces of the objectfrom the two-dimensional images, wherein each of the multiple surfacesincludes a corresponding plurality of data points, each having acorresponding measure of confidence; determining a correspondingconfidence threshold for each of the multiple surfaces; and enhancingeach of the multiple surfaces using an interpolation filter to modifydata points having a measure of confidence less than the correspondingconfidence threshold.
 2. The method of claim 1, wherein the systemparameters include: a number of surfaces included in the multiplesurfaces, a Z scan step size, a percentile threshold, a start Z scanposition, and a Z range of each of the multiple surfaces.
 3. The methodof claim 1, wherein the imaging system is one of a structured lightsource based imaging system and a confocal imaging system.
 4. The methodof claim 1, wherein the confidence threshold and the interpolationfilter are based on one of image contrast values and image intensityvalues.
 5. The method of claim 1, wherein each measure of confidencecomprises a contrast value.
 6. The method of claim 1, wherein eachmeasure of confidence comprises an intensity value.
 7. The method ofclaim 1, wherein each measure of confidence is determined from thetwo-dimensional images.
 8. The method of claim 1, wherein theinterpolation filter: identifies a first data point having a measure ofconfidence less than the confidence threshold; and identifies a firstset of one or more data points in a neighborhood surrounding the firstdata point, wherein the first set of one or more data points includeseach data point in the neighborhood having a measure of confidencegreater than or equal to the confidence threshold.
 9. The method ofclaim 8, wherein the interpolation filter modifies the first data pointbased on a weighted average determined from the first set of one or moredata points.
 10. The method of claim 9, wherein the interpolation filterraises the measure of confidence of the first data point.
 11. The methodof claim 8, wherein the system parameters define a size of theneighborhood.
 12. The method of claim 8, wherein each of the data pointshas a corresponding Z profile value, wherein the interpolation filtercalculates a weighted average of the Z profile values of the first setof one or more data points.
 13. The method of claim 8, wherein themeasure of confidence comprises a contrast value, wherein theinterpolation filter calculates a weighted average of the contrastvalues of the first set of one or more data points.
 14. The method ofclaim 8, wherein the measure of confidence comprises an intensity value,wherein the interpolation filter calculates a weighted average of theintensity values of the first set of one or more data points.
 15. Themethod of claim 1, further comprising determining the confidencethreshold for each surface based on the measures of confidence of thedata points of the surface.
 16. A method of detecting multi-surfaces ofan object, the method comprising: providing an imaging system capable ofdetecting surfaces of the object; setting up system parameters prior toimage acquisition; acquiring two-dimensional images at multiple Z stepsas defined by the system parameters; and extracting each surface of theobject in two steps: first, constructing the surface based on aconfidence threshold; and second, enhancing the surface using aninterpolation filter, wherein the confidence threshold and theinterpolation filter are based on one of image contrast values and imageintensity values, and wherein the interpolation filter is determined bya filter length and one of an image contrast percentile threshold and animage intensity percentile threshold.
 17. A method of detectingmulti-surfaces of an object, the method comprising: providing an imagingsystem capable of detecting surfaces of the object; setting up systemparameters prior to image acquisition; acquiring two-dimensional imagesat multiple Z steps as defined by the system parameters; and extractingeach surface of the object in two steps: first, constructing the surfacebased on a confidence threshold; and second, enhancing the surface usingan interpolation filter, wherein an enhanced surface includes first datapoints below the confidence threshold and second data points above theconfidence threshold, the second data points neighboring the first datapoints.
 18. The method of claim 17, wherein the first data points areeach assigned a new contrast and a new Z profile value derived from acontrast-weighted average of the second data points.
 19. The method ofclaim 17, wherein the first data points are each assigned a new contrastand a new Z profile value derived from an intensity-weighted average ofthe second data points.